Machine Learning Model for Predicting Pathological Invasiveness of Pulmonary Ground-Glass Nodules Based on AI-Extracted Radiomic Features.

Journal: Thoracic cancer
Published Date: Aug 1, 2025

Abstract

BACKGROUND: With the widespread adoption of low-dose CT screening, the detection of pulmonary ground-glass nodules (GGNs) has risen markedly, presenting diagnostic challenges in distinguishing preinvasive lesions from invasive adenocarcinomas (IAC). This study aimed to develop a machine learning (ML)-based model using artificial intelligence (AI)-extracted CT radiomic features to predict the invasiveness of GGNs.

Authors

  • Guozhen Yang
    Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, 100190, Beijing, China.
  • Yuanheng Huang
    Department of Cardiothoracic Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Huiguo Chen
    Department of Cardiothoracic Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Weibin Wu
    Division of Vascular Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510800, China; National-Guangdong Joint Engineering Laboratory for Diagnosis and Treatment of Vascular Disease, First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: wuweib@mail2.sysu.edu.cn.
  • Yonghui Wu
    Department of Health Outcomes and Biomedical Informatics.
  • Kai Zhang
    Anhui Province Key Laboratory of Respiratory Tumor and Infectious Disease, First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
  • Xiaojun Li
    Jiangsu CM Clinical Innovation Center of Degenerative Bone & Joint Disease, Wuxi TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Wuxi, China.
  • Jiannan Xu
    Department of Cardiothoracic Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Jian Zhang
    College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.

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